ACOUSTIC DETECTION MASK SYSTEMS AND/OR METHODS
    1.
    发明申请
    ACOUSTIC DETECTION MASK SYSTEMS AND/OR METHODS 审中-公开
    声学检测掩模系统和/或方法

    公开(公告)号:WO2012058727A2

    公开(公告)日:2012-05-10

    申请号:PCT/AU2011001417

    申请日:2011-11-04

    Abstract: Certain examples described herein relate to acoustic detection mask systems and/or methods. In certain examples, an acoustic detection mask system is provided. An example acoustic detection mask system includes a mask having a microphone located therein or thereon. The microphone is connected to a data logger that is configured to capture vibrations and/or sounds registered by the microphone. The data logger may store such information in a computer readable storage media thereof for subsequent analysis, e.g., via a computer program accessing such data after the data logger is connected to a separate computer system. The microphone may be positioned and the data analyzed so as to determine differences between oral and nasal breathing, as well as sleep-disordered breath and/or snoring. Such components may be provided as a part of a system or in any suitable combination or sub-combination. Associated methods also are described herein as a part of the technology.

    Abstract translation: 本文描述的某些示例涉及声学检测掩模系统和/或方法。 在某些示例中,提供了声学检测掩模系统。 示例性声学检测掩模系统包括具有位于其中或其上的麦克风的掩模。 麦克风连接到数据记录器,该数据记录器配置为捕获由麦克风注册的振动和/或声音。 数据记录器可以将这样的信息存储在其计算机可读存储介质中,用于随后的分析,例如,在数据记录器连接到单独的计算机系统之后,经由计算机程序访问这些数据。 可以定位麦克风并分析数据,以便确定口腔和鼻呼吸之间的差异,以及睡眠呼吸和/或打鼾。 这样的组件可以作为系统的一部分或以任何合适的组合或子组合提供。 本文中还描述了相关方法作为该技术的一部分。

    METHODS AND DEVICES WITH LEAK DETECTION
    2.
    发明申请
    METHODS AND DEVICES WITH LEAK DETECTION 审中-公开
    具有泄漏检测的方法和设备

    公开(公告)号:WO2012012835A3

    公开(公告)日:2012-03-22

    申请号:PCT/AU2011000950

    申请日:2011-07-28

    Abstract: Automated methods provide leak detection that may be implemented in a respiratory treatment apparatus. In some embodiments, the detection apparatus may automatically determine and score different types of leak events during a treatment session, including, for example, continuous mouth leak events and valve-like mouth leak events. The detection methodologies may be implemented as a data analysis of a specific purpose computer or a detection device that measures a respiratory airflow or a respiratory treatment apparatus that provides a respiratory treatment regime based on the detected leak. In some embodiments, the leak detector may determine and report a leak severity index. Such an index may combine data that quantifies different types of leak events.

    Abstract translation: 自动化方法提供可以在呼吸治疗设备中实施的泄漏检测。 在一些实施例中,检测装置可以在治疗期间自动确定并评分不同类型的泄漏事件,包括例如连续的口腔泄漏事件和瓣状口腔泄漏事件。 检测方法可以实施为测量呼吸气流的特定用途计算机或检测装置的数据分析或基于检测到的泄漏提供呼吸治疗方案的呼吸治疗装置。 在一些实施例中,泄漏检测器可以确定并报告泄漏严重性指数。 这样的指数可以组合量化不同类型的泄漏事件的数据。

    DISCRIMINATION OF CHEYNE -STOKES BREATHING PATTERNS BY USE OF OXIMETRY SIGNALS
    9.
    发明申请
    DISCRIMINATION OF CHEYNE -STOKES BREATHING PATTERNS BY USE OF OXIMETRY SIGNALS 审中-公开
    通过使用OXIMETRY信号识别CHEYNE-STOKES呼吸模式

    公开(公告)号:WO2010121290A1

    公开(公告)日:2010-10-28

    申请号:PCT/AU2010/000416

    申请日:2010-04-15

    Abstract: Methods and apparatus provide Cheyne-Stokes respiration ("CSR") detection based on a blood gas measurements such as oximetry. In some embodiments, a duration, such as a mean duration of contiguous periods of changing saturation or re- saturation occurring in an epoch taken from a processed oximetry signal, is determined. An occurrence of CSR may be detected from a comparison of the duration and a threshold derived to differentiate saturation changes due to CSR respiration and saturation changes due to obstructive sleep apnea. The threshold may be a discriminant function derived as a classifier by an automated training method. The discriminant function may be further implemented to characterize the epoch for CSR based on a frequency analysis of the oximetry data. Distance from the discriminant function may be utilized to generate probability values for the CSR detection.

    Abstract translation: 方法和装置基于血气测量(例如血氧定量法)提供Cheyne-Stokes呼吸(“CSR”)检测。 在一些实施例中,确定持续时间,例如在从经处理的血氧测定信号取得的时期中发生的改变饱和度或再饱和的连续周期的平均持续时间。 可以从持续时间和导出的阈值的比较来检测CSR的发生,以区分由于呼吸呼吸引起的饱和度变化和由于阻塞性睡眠呼吸暂停引起的饱和度变化。 阈值可以是通过自动训练方法作为分类器导出的判别函数。 基于氧饱和度数据的频率分析,可以进一步实现判别函数以表征CSR历元。 可以利用与判别函数的距离来生成CSR检测的概率值。

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